High Order Feedback-Feedforward Iterative Learning Control Scheme with a Variable Forgetting Factor

Abstract

In this work, we present a new iterative learning control (ILC) scheme for a class of non-linear systems with uncertain and non-repetitive disturbances, in order to achieve perfect tracking by proposing a high order feedback-feedforward ILC algorithm with a variable forgetting factor. The high order feedback-feedforward iterative learning controller can fully apply the previous control data to the system, which allows the system to track expectations more rapidly and precisely. Introducing a variable forgetting factor can weaken the former control output and its variance in the control law, while strengthening the robustness of the ILC. Through rigorous analyses, we demonstrate that uniform convergence of the state tracking error is guaranteed under this new ILC scheme. Simulation examples are also included to demonstrate the feasibility and effectiveness of the proposed learning controls.